Vetted Dashboard Development Professionals

shivapriya pillalamarri

Screened

Mid-level AI/ML Engineer specializing in financial analytics and production ML systems

Boston, MA4y exp

“Analytics candidate with experience in financial transaction and fraud detection projects, combining SQL data preparation, Python-based automation, and dashboarding. They have owned projects from stakeholder alignment and metric definition through rollout, with emphasis on reducing false positives, improving operational efficiency, and making analytics outputs easy for business teams to adopt.”

“Analytics professional at Santander Bank with hands-on experience building SQL and Python workflows for transaction reporting, reconciliation, and monitoring across messy multi-source financial data. They combine strong data validation and exception-handling practices with stakeholder-friendly dashboards, and also bring digital analytics experience from a Google Analytics UI optimization project focused on funnel drop-off and engagement.”

“Analytics professional with healthcare and operations experience who turns messy enterprise data from platforms like Teradata, GCP, SQL Server, and Snowflake into trusted reporting layers and reproducible analysis workflows. They combine SQL, Python, PySpark, Power BI, and Tableau to improve reporting accuracy and performance, including a 30% dashboard refresh improvement and 20-25% accuracy gains in healthcare reporting.”

“Analytics professional with Dell experience unifying global online sales, web analytics, SAP, and planning data across 20+ countries into scalable reporting pipelines and Power BI dashboards. Stands out for combining deep SQL/ETL work with Python automation, KPI design, and experimentation—delivering measurable outcomes like 80% less manual effort, a 2% conversion lift worth millions, and faster business decision-making.”

“Enterprise-minded builder who has owned complex, high-impact systems from discovery through stabilization, including a customer master data platform at AB InBev serving 2,000 sales reps across 13 countries. Also demonstrates strong AI product instincts, having built a first-place ReAct-style NYC property intelligence agent at IBM's AI Demystified Hackathon, while showing deep rigor in data quality, integrations, and production reliability.”

“Wayfair ML/AI engineer who has shipped and operated production LLM systems for both internal analytics and customer-facing assistants. Stands out for combining strong RAG/retrieval engineering with production-grade platform work—improving trust, reducing latency by ~30%, and cutting ad hoc reporting demand by ~50%.”

Aditya Rao

Screened

Mid-level Software Engineer specializing in backend, AI, and distributed systems

San Jose, CA5y exp

“Software engineer with 4.5 years of startup experience across programmatic advertising, health tech e-commerce, and automobile diagnostics, plus both bachelor's and master's degrees in CSE. Built an agentic global supply chain platform in a hackathon using a highly structured AI-first workflow, and has hands-on experience designing multi-agent debate systems, rollout safeguards, and observability-driven production fixes.”

“Project/operations leader who has grown from owning small implementation projects to running PMO governance on a multi-year SAP migration at Accenture, and now independently delivers end-to-end automation, CRM, and workflow projects for 4-5 freelance clients across varied industries. Stands out for blending hands-on systems building with strong executive reporting, risk management, and client enablement.”

Brian Lam

Screened

Mid-level Full-Stack Engineer specializing in AI and FinTech

New York, NY12y exp

“Frontend-focused engineer working in Next.js/React who has owned complex internal and partner-facing browser workflows, including a multi-step CSV onboarding wizard and an admin quiz editor. Stands out for practical debugging skill on production-only timezone issues and for building resilient, typed form-heavy UIs with strong error handling.”

“AI/full-stack engineer who has built a production-style LLM knowledge assistant from scratch, combining FastAPI, LangChain, FAISS, semantic retrieval, and a user-facing chat interface. Stands out for owning both the technical architecture and the product usability layer, including latency optimization, prompt refinement, and source-backed responses to improve trust for non-technical users.”

Leah Kenehan

Screened

Senior CRM & Lifecycle Marketing Manager specializing in retention and customer growth

Valencia, CA19y exp

“Senior CRM/lifecycle marketer who has grown from email execution into full-funnel campaign ownership, spanning strategy, segmentation, deployment, and performance analysis. Brings hands-on experience with Simon Data, Iterable, and Moveable Ink, plus interim people leadership managing a team of six while driving retention and rebooking campaigns in a travel/hospitality-style DTC environment.”

“Verizon engineer who productionized an LLM-based personalization capability for a customer-facing digital platform, owning the path from success metrics through scalable APIs, A/B validation, and post-launch monitoring (latency/accuracy/drift). Experienced in diagnosing and fixing real-time LLM/RAG workflow issues under peak load, and in enabling adoption via tailored technical demos/workshops and sales support materials.”

“Backend engineer with experience evolving a real-time transaction and rewards processing platform from a tightly coupled architecture into domain-based microservices. Uses REST plus Kafka for synchronous vs. asynchronous workflows, and builds Python/FastAPI APIs with Pydantic contracts, Docker/Kubernetes deployments, and JWT/OAuth-based security; has also supported analytics/dashboard use cases (Power BI).”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“Healthcare operator/strategic program lead currently at Time Care working directly with national health insurance payer partners to translate executive priorities into operational initiatives (e.g., patient marketing, data interoperability, in-home member access). Previously led the build-out and day-to-day execution of a new value-based performance risk division at Stellar Health, combining metrics-driven operations with long-term strategy and executive alignment.”

“Built an end-to-end Alexa smart-home IoT application controlling a Wi-Fi bulb, including ESP32 firmware (MQTT) and an AWS serverless backend (IoT Core/Device Shadow, Lambda, DynamoDB) with a REST API. Demonstrates strong real-time scalability patterns (streaming ingestion, stateless processing, partition-key design) and full-stack delivery with Spring Boot + React (JWT auth, CORS, data-heavy dashboards).”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

“Customer Success leader with deep ad tech expertise who owned enterprise accounts end-to-end, including Bank of America, driving ad fraud down from ~8–10% to <1% in ~3 months and resolving spikes through data-driven investigations. Experienced partnering cross-functionally as a PM to build analytics dashboards and using user stories to align Product/Engineering/Sales and influence roadmap priorities; also supports land-and-expand via paid benchmarking/industry data upsells.”

Divyansh Agarwal

Screened

Junior Machine Learning Engineer specializing in computer vision and LLM applications

New York, NY3y exp

“Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).”

“Healthcare analytics candidate with hands-on experience turning messy claims data in Redshift and S3 into validated reporting tables, plus automating KPI workflows in Python. They’ve owned end-to-end operational analytics projects, including a claims delay analysis that improved processing efficiency by about 20%, and have experience driving stakeholder adoption of standardized metrics across dashboards.”

MOUNIKA SAI MEKALA

Screened

Junior Data Analyst specializing in financial and operational analytics

Kansas, USA3y exp

“Analytics professional with experience at KPMG turning messy operational and financial data from SQL Server and AWS S3 into clean reporting datasets and automated Python workflows. They combine SQL, Python, Power BI, and experimentation methods to deliver stakeholder-aligned KPI dashboards and marketing performance insights with a strong focus on data integrity and reproducibility.”